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DOE OSTI2014

Hubble space telescope emission line galaxies at z ∼ 2: the Lyα escape fraction

We compare the Hβ line strengths of 1.90 < z < 2.35 star-forming galaxies observed with the near-IR grism of the Hubble Space Telescope with ground-based measurements of Lyα from the HETDEX Pilot Survey and narrow-band imaging. By examining the line ratios of 73 galaxies, we show that most star-forming systems at this epoch have a Lyα escape fraction below ∼6%. We confirm this result by using stellar reddening to estimate the effective logarithmic extinction of the Hβ emission line (c {sub Hβ} = 0.5) and measuring both the Hβ and Lyα luminosity functions in a ∼100, 000 Mpc{sup 3} volume of space. We show that in our redshift window, the volumetric Lyα escape fraction is at most 4.4{sub −1.2}{sup +2.1}%, with an additional systematic ∼25% uncertainty associated with our estimate of extinction. Finally, we demonstrate that the bulk of the epoch's star-forming galaxies have Lyα emission line optical depths that are significantly greater than that for the underlying UV continuum. In our predominantly [O III] λ5007-selected sample of galaxies, resonant scattering must be important for the escape of Lyα photons.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Ultraviolet H 2 luminescence in molecular clouds induced by cosmic rays

Context. Galactic cosmic rays (CRs) play a crucial role in ionisation, dissociation, and excitation processes within dense cloud regions where UV radiation is absorbed by dust grains and gas species. CRs regulate the abundance of ions and radicals, leading to the formation of more and more complex molecular species, and determine the charge distribution on dust grains. A quantitative analysis of these effects is essential for understanding the dynamical and chemical evolution of star-forming regions. Aims. The CR-induced photon flux has a significant impact on the evolution of the dense molecular medium in its gas and dust components. This study evaluates the flux of UV photons generated by CRs to calculate the photon-induced dissociation and ionisation rates of a vast number of atomic and molecular species, as well as the integrated UV photon flux. Methods. To achieve these goals, we took advantage of recent developments in the determination of the spectra of secondary electrons, in the calculation of state-resolved excitation cross sections of H 2 by electron impact, and of photodissociation and photoionisation cross sections. Results. We calculated the H 2 level population of each rovibrational level of the X, B, C, B′, D, B″, D′, and a states. We then computed the UV photon spectrum of H 2 in its line and continuum components between 72 and 700 nm, with unprecedented accuracy, as a function of the CR spectrum incident on a molecular cloud, the H 2 column density, the isomeric H 2 composition, and the dust properties. The resulting photodissociation and photoionisation rates are, on average, lower than previous determinations by a factor of about 2, with deviations of up to a factor of 5 for the photodissociation of species such as AlH, C 2 H 2 , C 2 H 3 , C 3 H 3 , LiH, N 2 , NaCl, NaH, O 2 + , S2, SiH, l-C 4 , and l-C 5 H. A special focus is given to the photoionisation rates of H 2 , HF, and N 2 , as well as to the photodissociation of H 2 , which we find to be orders of magnitude higher than previous estimates. We give parameterisations for both the photorates and the integrated UV photon flux as a function of the CR ionisation rate, which implicitly depends on the H 2 column density, as well as the dust properties.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

“Third-body” collision parameters for hydrocarbons, alcohols, and hydroperoxides and an effective internal rotor approach for estimating them

Collision rate constants and third-body collision efficiencies are calculated for more than 300 alkanes, alcohols, and hydroperoxides, for the bath gases He, Ar, H 2 , and N 2 , and from 300 to 2000 K. The data set includes highly branched species and species with as many as 16 nonhydrogen atoms N , and it is analyzed to develop strategies for estimating collision properties more generally. Simple analytic formulas describing the Lennard-Jones collision parameters $σ$ and $ε$ are obtained for each of the three classes of systems as a function of N . Trends in the collision efficiency range parameter $α$ = $\langle$Δ E d $\rangle$ are more complicated, and a method is developed and validated for estimating $α$ based on the numbers and types of internal rotors and oxygen-containing groups. Specifically, the approach maps the expected value of $α$ for a branched alkane, alcohol, or hydroperoxide onto those for the corresponding normal (linear) series via an effective number of nonhydrogen atoms N eff . The prescription for N eff is based on counting internal rotor types and is shown to be insensitive to temperature and fairly insensitive to the identity of the bath gas. Together, these strategies allow for the ready estimation of the collision parameters $σ$, $ε$, and $α$ so long as results for the associated linear series are available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Ultraviolet H 2 luminescence in molecular clouds induced by cosmic rays

Context. Galactic cosmic rays (CRs) play a crucial role in ionisation, dissociation, and excitation processes within dense cloud regions where UV radiation is absorbed by dust grains and gas species. CRs regulate the abundance of ions and radicals, leading to the formation of more and more complex molecular species, and determine the charge distribution on dust grains. A quantitative analysis of these effects is essential for understanding the dynamical and chemical evolution of star-forming regions. Aims. The CR-induced photon flux has a significant impact on the evolution of the dense molecular medium in its gas and dust components. This study evaluates the flux of UV photons generated by CRs to calculate the photon-induced dissociation and ionisation rates of a vast number of atomic and molecular species, as well as the integrated UV photon flux. Methods. To achieve these goals, we took advantage of recent developments in the determination of the spectra of secondary electrons, in the calculation of state-resolved excitation cross sections of H 2 by electron impact, and of photodissociation and photoionisation cross sections. Results. We calculated the H 2 level population of each rovibrational level of the X, B, C, B', D, B", D', and a states. We then computed the UV photon spectrum of H 2 in its line and continuum components between 72 and 700 nm, with unprecedented accuracy, as a function of the CR spectrum incident on a molecular cloud, the H 2 column density, the isomeric H 2 composition, and the dust properties. The resulting photodissociation and photoionisation rates are, on average, lower than previous determinations by a factor of about 2, with deviations of up to a factor of 5 for the photodissociation of species such as AlH, C 2 H 2 , C 2 H 3 , C 3 H 3 , LiH, N 2 , NaCl, NaH, O$^+_2$, S 2 , SiH, l-C 4 , and l-C 5 H. A special focus is given to the photoionisation rates of H 2 , HF, and N 2 , as well as to the photodissociation of H 2 , which we find to be orders of magnitude higher than previous estimates. We give parameterisations for both the photorates and the integrated UV photon flux as a function of the CR ionisation rate, which implicitly depends on the H 2 column density, as well as the dust properties.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Predicting the redshift 2 Hα luminosity function using [O iii] emission line galaxies

Upcoming space-based surveys such as Euclid and WFIRST-AFTA plan to measure baryonic acoustic oscillations in order to study dark energy. These surveys will use IR slitless grism spectroscopy to measure redshifts of a large number of galaxies over a significant redshift range. In this paper, we use the Wide Field Camera 3 Infrared Spectroscopic Parallel Survey (WISP) to estimate the expected number of Hα emitters observable by these future surveys. WISP is an ongoing Hubble Space Telescope slitless spectroscopic survey, covering the 0.8–1.65 μm wavelength range and allowing the detection of Hα emitters up to z ∼ 1.5 and [O iii] emitters to z ∼ 2.3. We derive the Hα–[O iii] bivariate line luminosity function (LLF) for WISP galaxies at z ∼ 1 using a maximum likelihood estimator that properly accounts for uncertainties in line luminosity measurements and we demonstrate how it can be used to derive the Hα luminosity function by exclusively fitting [O iii] data. Using the z∼2 [O iii] LLF and assuming that the relation between Hα and [O iii] luminosity does not change significantly over the redshift range, we predict the Hα number counts at z∼2—the upper end of the redshift range of interest for future surveys. For the redshift range 0.7<z<2, we expect ∼3000 galaxies deg{sup −2} for a flux limit of 3 × 10{sup −16} erg s{sup −1} cm{sup −2} (the proposed depth of the Euclid galaxy redshift survey) and ∼20,000 galaxies deg{sup −2} for a flux limit of ∼10{sup −16} erg s{sup −1} cm{sup −2} (the baseline depth of the WFIRST galaxy redshift survey).

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Low dose radiation risks for women surviving the a-bombs in Japan: generalized additive model

Background: Analyses of cancer mortality and incidence in Japanese A-bomb survivors have been used to estimate radiation risks, which are generally higher for women. Relative Risk (RR) is usually modelled as a linear function of dose. Extrapolation from data including high doses predicts small risks at low doses. Generalized Additive Models (GAMs) are flexible methods for modelling non-linear behaviour. Methods: GAMs are applied to cancer incidence in female low dose subcohorts, using anonymous public data for the 1958 – 1998 Life Span Study, to test for linearity, explore interactions, adjust for the skewed dose distribution, examine significance below 100 mGy, and estimate risks at 10 mGy. Results: For all solid cancer incidence, RR estimated from 0 – 100 mGy and 0 – 20 mGy subcohorts is significantly raised. The response tapers above 150 mGy. At low doses, RR increases with age-at-exposure and decreases with time-since-exposure, the preferred covariate. Using the empirical cumulative distribution of dose improves model fit, and capacity to detect non-linear responses. RR is elevated over wide ranges of covariate values. Results are stable under simulation, or when removing exceptional data cells, or adjusting neutron RBE. Estimates of Excess RR at 10 mGy using the cumulative dose distribution are 10 – 45 times higher than extrapolations from a linear model fitted to the full cohort. Below 100 mGy, quasipoisson models find significant effects for all solid, squamous, uterus, corpus, and thyroid cancers, and for respiratory cancers when age-at-exposure > 35 yrs. Results for the thyroid are compatible with studies of children treated for tinea capitis, and Chernobyl survivors. Results for the uterus are compatible with studies of UK nuclear workers and the Techa River cohort. Conclusion: Non-linear models find large, significant cancer risks for Japanese women exposed to low dose radiation from the atomic bombings. The risks should be reflected in protection standards.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

DESI DR2 Baryon Acoustic Oscillations from the Lyman Alpha Forest Multipoles

We present an alternative measurement of the Baryon Acoustic Oscillation (BAO) using the Legendre multipole representation of the Ly$α$ forest correlation functions from the second data release (DR2) of the Dark Energy Spectroscopic Instrument survey. Compressing the auto- and cross-correlation functions into Legendre multipoles yields a positive-definite covariance matrix without any smoothing -- unlike the baseline DR2 analysis -- thanks to a significantly reduced data vector size. We introduce the statistical corrections required to debias the finite-sample covariance matrix estimate and demonstrate that monopole and quadrupole terms for both auto- and cross-correlations can be used even when the correlation functions are distorted by continuum errors and contaminated by metals. This formalism has slightly diminished the constraining power of the BAO scale, while considerably weakening constraints on nuisance parameters. We measure the isotropic BAO scale with $0.93\%$ precision at $z_\mathrm{eff}=2.35$, the Hubble parameter $H(z_\mathrm{eff})=(239.5\pm3.4)~(147.09~\mathrm{Mpc}/r_d) ~\mathrm{km~s}^{-1}~\text{Mpc}^{-1}$, and the transverse comoving distance $D_M(z_\mathrm{eff})=(5.80 \pm 0.10)~(r_d/147.09~\mathrm{Mpc})$~Gpc for a given value of the sound horizon ($r_d$). Our BAO results are entirely consistent with the baseline DR2 analysis.

Karaçaylı, Naim Göksel [Chicago U., KICP; Ohio Sta↗

DOE OSTI2016

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

Purpose: A previously proposed method to reduce radiation dose to patient in dynamic contrast-enhanced (DCE) CT is enhanced by principal component analysis (PCA) filtering which improves the signal-to-noise ratio (SNR) of time-concentration curves in the DCE-CT study. The efficacy of the combined method to maintain the accuracy of kinetic parameter estimates at low temporal resolution is investigated with pixel-by-pixel kinetic analysis of DCE-CT data. Methods: The method is based on DCE-CT scanning performed with low temporal resolution to reduce the radiation dose to the patient. The arterial input function (AIF) with high temporal resolution can be generated with a coarsely sampled AIF through a previously published method of AIF estimation. To increase the SNR of time-concentration curves (tissue curves), first, a region-of-interest is segmented into squares composed of 3 × 3 pixels in size. Subsequently, the PCA filtering combined with a fraction of residual information criterion is applied to all the segmented squares for further improvement of their SNRs. The proposed method was applied to each DCE-CT data set of a cohort of 14 patients at varying levels of down-sampling. The kinetic analyses using the modified Tofts’ model and singular value decomposition method, then, were carried out for each of the down-sampling schemes between the intervals from 2 to 15 s. The results were compared with analyses done with the measured data in high temporal resolution (i.e., original scanning frequency) as the reference. Results: The patients’ AIFs were estimated to high accuracy based on the 11 orthonormal bases of arterial impulse responses established in the previous paper. In addition, noise in the images was effectively reduced by using five principal components of the tissue curves for filtering. Kinetic analyses using the proposed method showed superior results compared to those with down-sampling alone; they were able to maintain the accuracy in the quantitative histogram parameters of volume transfer constant [standard deviation (SD), 98th percentile, and range], rate constant (SD), blood volume fraction (mean, SD, 98th percentile, and range), and blood flow (mean, SD, median, 98th percentile, and range) for sampling intervals between 10 and 15 s. Conclusions: The proposed method of PCA filtering combined with the AIF estimation technique allows low frequency scanning for DCE-CT study to reduce patient radiation dose. The results indicate that the method is useful in pixel-by-pixel kinetic analysis of DCE-CT data for patients with cervical cancer.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2023

Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making

One of the great challenges with decision making tasks on real world systems is the fact that data is sparse and acquiring additional data is expensive. In these cases, it is often crucial to make a model of the environment to assist in making decisions. At the same time, limited data means that learned models are erroneous, making it just as important to equip the model with good predictive uncertainties. In the context of learning sequential decision making policies, these uncertainties can prove useful for informing which data to collect for the greatest improvement in policy performance \citep{mehta2021experimental, mehta2022exploration} or informing the policy about unsure regions of state and action space to avoid during test time \citep{yu2020mopo}. Additionally, assuming that realistic samples of the environment can be drawn, an adaptable policy can be trained that attempts to make optimal decisions for any given possible instance of the environment \citep{ghosh2022offline, chen2021offline}. In this work, we examine the so-called ``probabilistic neural network'' (PNN) model that is ubiquitous in model-based reinforcement learning (MBRL) works. We argue that while PNN models may have good marginal uncertainties, they form a distribution of non-smooth transition functions. Not only are these samples unrealistic and may hamper adaptability, but we also assert that this leads to poor uncertainty estimates when predicting multiple step trajectory estimates. To address this issue, we propose a simple sampling method that can be implemented on top of pre-existing models.We evaluate our sampling technique on a number of environments, including a realistic nuclear fusion task, and find that, not only do smooth transition function samples produce more calibrated uncertainties, but they also lead to better downstream performance for an adaptive policy.

Offline Reinforcement Learning↗

DOE OSTI2025

Secure State Estimation with Asynchronous Measurements for Coordinated Cyber Attack Detection in Active Distribution Systems

Coordinated cyber attacks tamper with measurement data to disrupt the situational awareness of active distribution systems. Various sensors report measurements asynchronously at different rates, which introduces challenges during state estimation. In addition, this forces cyber intruders to exert greater effort to compromise multiple communication channels and launch coordinated attacks. Therefore, multi-channel and asynchronous measurements could be harnessed to develop more secure cyber defense strategies. In this paper, a prediction-correction-based multi-rate observer is designed to exploit the value of asynchronous measurements for the detection of coordinated false data injection (FDI) attacks. First, a time-function-dependent prediction-correction strategy is proposed to adjust the sampling interval for each sensor’s measurement. Then, an observer is designed based on the trade-off between estimation error and the optimal period of the most recent sampling instant, with the convergence of estimation error with the maximum permitted sampling interval. Moreover, the conditions for exponential stability are developed using the Lyapunov–Krasovskii functional technique. Next, a coordinated FDI attack detection strategy is developed based on the dual nonlinear minimization problem. The proposed attack detection and secure state estimation strategies are tested on the IEEE 13-node system. Simulation results show that these schemes are effective in enhancing attack detection based on asynchronous measurements or compromised data.

asynchronous measurements↗

DOE OSTI2014

First principle study of elastic and thermodynamic properties of ZrZn{sub 2} and HfZn{sub 2} under high pressure

A comprehensive investigation of the structural, elastic, and thermodynamic properties for Laves-phases ZrZn{sub 2} and HfZn{sub 2} are conducted using density functional total energy calculations combined with the quasi-harmonic Debye model. The optimized lattice parameters of ZrZn{sub 2} and HfZn{sub 2} compare well with available experimental values. We estimated the mechanical behaviors of both compounds under compression, including mechanical stability, Young's modulus, Poisson's ratio, ductility, and anisotropy. Additionally, the thermodynamic properties as a function of pressure and temperature are analyzed and found to be in good agreement with the corresponding experimental data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

Development of a bayesian belief network model for the software reliability assessment of nuclear digital I and C safety systems - 206

Since the digital instrumentation and control systems are expected to play an important role for the safety systems in nuclear power plants (NPPs), the need has emerged to not only establish a basis for incorporating software behavior into digital I and C system reliability models, but also to quantify the failure probability of the software used in NPP digital protection systems. In this study, a Bayesian belief network (BBN) model is developed to quantitatively assess software reliability by estimating the number of faults in a software program considering its software development life cycle (SDLC). The model structure and parameters are established based on the information applicable to NPP safety-related systems and the evidence used to construct and quantify the BBN model was collected from three stages of expert elicitation. The software failure probability is estimated from the number of residual defects in a software program at the end of SDLC phase. As a case study, the BBN model was applied to quantify the software reliability of a typical digital protection software having the size of 50 function points and having the Medium development and validation and verification (V and V) qualities. The developed model can be applied to estimate the failure probability for both developing and deployed safety-related NPP software, and such results can be used to evaluate the quality of the digital I and C systems in addition to estimating potential reactor risk due to software failure. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

sp 3 -Functionalization of Single-Walled Carbon Nanotubes Creates Localized Spins

Chemical functionalization-introduced sp 3 quantum defects in single-walled carbon nanotubes (SWCNTs) have shown compelling optical properties for their potential applications in quantum information science and bioimaging. Here, we utilize temperature- and power-dependent electron spin resonance measurements to study the fundamental spin properties of SWCNTs functionalized with well-controlled densities of sp 3 quantum defects. Signatures of isolated spins that are highly localized at the sp 3 defect sites are observed, which we further confirm with density functional theory calculations. Applying temperature-dependent line width analysis and power-saturation measurements, we estimate the spin–lattice relaxation time T 1 and spin dephasing time T 2 to be around 9 μs and 40 ns, respectively. Furthermore, these findings of the localized spin states that are associated with the sp 3 quantum defects not only deepen our understanding of the molecular structures of the quantum defects but could also have strong implications for their applications in quantum information science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Model predictive combustion control of a Gasoline Compression Ignition engine

Gasoline Compression Ignition is a novel combustion concept that derives its superiority from the high compression ratio of a compression ignition engine as well as the properties of gasoline fuel, such as longer ignition delay and higher volatility compared to diesel. Here, this combustion concept was experimentally tested on a 12.4L Class 8 truck engine. Based on these experimental data, prior efforts by the authors focused on the development of an engine model for a heavy-duty engine operating on a low-reactivity fuel. This engine model was leveraged within this study to investigate a combustion control strategy at different engine conditions and injection methods and was augmented to incorporate cycle-to-cycle combustion variations. State estimation is performed by means of a Kalman filter which feeds into a model predictive controller. The model predictive controller chooses control actions based on a predefined cost function under consideration of bounds reflecting physical constraints. The engine model was utilized to establish a state-space model that serves the Kalman filter and model predictive controller for estimation and prediction. A comparative study investigating control actions and engine behavior was performed with and without limiting in-cylinder peak pressure as well as combustion noise, which is of particular interest for early pilot injection strategies. In addition, the proposed control architecture was investigated at two different levels of cycle-to-cycle variations and compared to the performance of a control structure with input disturbance rejection. For increased cycle-to-cycle variations, disturbance estimation reduces state fluctuations and control effort. In general, this investigation highlights control aspects specific to a compression-ignited combustion regime with low-reactivity fuel. The control algorithm is able to maintain the desired references for brake mean effective pressure and combustion phasing while controlling peak in-cylinder pressure and combustion noise.

42 ENGINEERING↗

DOE OSTI2019

A multiscale approach to modeling carbon cycling within a high-elevation watershed (Final Report)

This report is to summarize the technical work completed during the project entitled "A Multiscale Approach to Modeling Carbon and Nitrogen Cycling within a High Elevation Watershed" (Award: DE-SC0014556). The report covers work completed between 08/01/2015 and 07/31/2018. The overarching objective of the work was to conduct a multi-scale reactive transport investigation of C and N in a high-elevation catchment in Colorado for the purposes of evaluating modeling approaches for describing net fluxes of C and N and their relationship to measurable hydrologic parameters. To accomplish this objective, we developed two microcatchment observatories where we have characterized the inventories of carbon and nitrogen and the fluxes of carbon into and out of the soil zone in tandem with environmental parameters, including soil moisture and temperature. We further evaluated watershed-scale carbon fluxes using measured concentration-discharge observations. To characterize the inventories of carbon and nitrogen, we developed a new approach for molecular characterization of soil organic carbon functional group abundance (SOC-fga) and applied this approach to bulk soil and density fractions from 4 profiles across the catchments. The speciation data was paired with radiocarbon and NMR measurements. To characterize fluxes, soil gas concentrations and carbon dioxide flux measurements were made throughout three growing seasons (2016, 2017, 2018) and combined with ground-based and remote sensing estimates of plant phenology. Paired laboratory studies investigated the response of microbial respiration to changing soil moisture as a function of soil depth and landscape position. Two modeling approaches have been developed. The first approach uses a diffusion-reaction approach to calculate the depth profile of carbon dioxide production rates based on measured profiles and surface efflux rates. The second modeling approach, a dormancy-enabled microbial model describes the microbial response to soil moisture by accounting for two distinct microbial strategists.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Dynamic Programming and Error Estimates for Stochastic Control Problems with Maximum Cost

This work is concerned with stochastic optimal control for a running maximum cost. A direct approach based on dynamic programming techniques is studied leading to the characterization of the value function as the unique viscosity solution of a second order Hamilton–Jacobi–Bellman (HJB) equation with an oblique derivative boundary condition. A general numerical scheme is proposed and a convergence result is provided. Error estimates are obtained for the semi-Lagrangian scheme. These results can apply to the case of lookback options in finance. Moreover, optimal control problems with maximum cost arise in the characterization of the reachable sets for a system of controlled stochastic differential equations. Some numerical simulations on examples of reachable analysis are included to illustrate our approach.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Physics-constrained deep neural network method for estimating parameters in a redox flow battery

Here, in this paper, we present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium redox flow battery (VRFB). In this approach, we use deep neural networks to approximate the model parameters as functions of the operating conditions. This method allows the integration of VRFB computational models as the physical constraints in the parameter learning process, leading to enhanced accuracy of parameter estimation and cell voltage prediction. Using an experimental dataset, we demonstrate that the PCDNN method can estimate model parameters for a range of operating conditions and improve the 0D model prediction of voltage compared to the 0D model prediction with constant operation-condition-independent parameters estimated with traditional inverse methods. We also demonstrate that the PCDNN approach has an improved generalization ability for estimating parameter values for operating conditions not used in the training process.

25 ENERGY STORAGE↗

DOE OSTI2016

Graphene oxide and adsorption of chloroform: A density functional study

Chlorinated hydrocarbon compounds are of environmental concerns, since they are toxic to humans and other mammals, and are widespread, and exposure is hard to avoid. Understanding and improving methods to reduce the amount of the substances are important. We present an atomic-scale calculational study of the adsorption of chlorine-based substance chloroform (CHCl{sub 3}) on graphene oxide, as a step in estimating the capacity of graphene oxide for filtering out such substances, e.g., from drinking water. The calculations are based on density functional theory, and the recently developed consistent-exchange functional for the van der Waals density-functional method is employed. We obtain values of the chloroform adsorption energy varying from roughly 0.2 to 0.4 eV per molecule. This is comparable to previously found results for chloroform adsorbed directly on clean graphene, using similar calculations. In a wet environment, like filters for drinking water, the graphene will not stay clean and will likely oxidize, and thus adsorption onto graphene oxide, rather than clean graphene, is a more relevant process to study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗